{"id":"https://openalex.org/W3096516513","doi":"https://doi.org/10.1109/ita50056.2020.9244996","title":"Identifying unpredictable test examples with worst-case guarantees","display_name":"Identifying unpredictable test examples with worst-case guarantees","publication_year":2020,"publication_date":"2020-02-02","ids":{"openalex":"https://openalex.org/W3096516513","doi":"https://doi.org/10.1109/ita50056.2020.9244996","mag":"3096516513"},"language":"en","primary_location":{"id":"doi:10.1109/ita50056.2020.9244996","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ita50056.2020.9244996","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Information Theory and Applications Workshop (ITA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110776230","display_name":"Shafi Goldwasser","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shafi Goldwasser","raw_affiliation_strings":["Berkeley University and MIT"],"affiliations":[{"raw_affiliation_string":"Berkeley University and MIT","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086904495","display_name":"Adam Tauman Kalai","orcid":"https://orcid.org/0000-0002-4559-8574"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Adam Tauman Kalai","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081254473","display_name":"Yael Tauman Kalai","orcid":"https://orcid.org/0009-0002-9406-7734"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yael Tauman Kalai","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077061756","display_name":"Omar Montasser","orcid":null},"institutions":[{"id":"https://openalex.org/I160992636","display_name":"Toyota Technological Institute at Chicago","ror":"https://ror.org/02sn5gb64","country_code":"US","type":"education","lineage":["https://openalex.org/I160992636"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Omar Montasser","raw_affiliation_strings":["TTI Chicago"],"affiliations":[{"raw_affiliation_string":"TTI Chicago","institution_ids":["https://openalex.org/I160992636"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5110776230"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.13827975,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6915301084518433},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6644843816757202},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.647229015827179},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.6402941346168518},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.6390237808227539},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5655806064605713},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49756911396980286},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4858102798461914},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.4749983549118042},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.369093656539917},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3504748046398163},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3406766653060913},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2667196989059448}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6915301084518433},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6644843816757202},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.647229015827179},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.6402941346168518},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6390237808227539},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5655806064605713},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49756911396980286},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4858102798461914},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.4749983549118042},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.369093656539917},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3504748046398163},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3406766653060913},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2667196989059448},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ita50056.2020.9244996","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ita50056.2020.9244996","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Information Theory and Applications Workshop (ITA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.46000000834465027,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W9657784","https://openalex.org/W171557998","https://openalex.org/W1673923490","https://openalex.org/W1945616565","https://openalex.org/W2019363670","https://openalex.org/W2029538739","https://openalex.org/W2069588275","https://openalex.org/W2108018401","https://openalex.org/W2108428725","https://openalex.org/W2112483442","https://openalex.org/W2118259999","https://openalex.org/W2147947791","https://openalex.org/W2154952480","https://openalex.org/W2162651021","https://openalex.org/W2339303619","https://openalex.org/W2402957931","https://openalex.org/W2799032899","https://openalex.org/W2803732607","https://openalex.org/W2892179671","https://openalex.org/W2897297967","https://openalex.org/W2898193427","https://openalex.org/W2913266441","https://openalex.org/W2915022044","https://openalex.org/W2962729158","https://openalex.org/W2962972504","https://openalex.org/W2963207607","https://openalex.org/W2963289726","https://openalex.org/W2963496101","https://openalex.org/W2963564844","https://openalex.org/W2963775761","https://openalex.org/W2964153729","https://openalex.org/W2964253222","https://openalex.org/W2964556751","https://openalex.org/W3103836116","https://openalex.org/W3124277475","https://openalex.org/W4238284510","https://openalex.org/W4238893454","https://openalex.org/W4293846201","https://openalex.org/W6637162671","https://openalex.org/W6640425456","https://openalex.org/W6676264761","https://openalex.org/W6676840641","https://openalex.org/W6703635181","https://openalex.org/W6739868092","https://openalex.org/W6750707585","https://openalex.org/W6751762734","https://openalex.org/W6754848437","https://openalex.org/W6755485836","https://openalex.org/W6755829279","https://openalex.org/W6759129252","https://openalex.org/W6759410175","https://openalex.org/W6765694979"],"related_works":["https://openalex.org/W3211393740","https://openalex.org/W3208049411","https://openalex.org/W3022908591","https://openalex.org/W4285706568","https://openalex.org/W209733029","https://openalex.org/W2891480213","https://openalex.org/W2991483587","https://openalex.org/W3118953353","https://openalex.org/W2158542502","https://openalex.org/W1997978958"],"abstract_inverted_index":{"Often":[0],"times,":[1],"whether":[2],"it":[3],"be":[4,37],"for":[5],"adversarial":[6,84],"or":[7,48],"natural":[8],"reasons,":[9],"the":[10,68,101],"distributions":[11,97],"of":[12,25,32,62,64,80,90],"test":[13,28,33,96],"and":[14,27,77,85,95],"training":[15,26,94],"data":[16],"differ.":[17],"We":[18],"give":[19],"an":[20],"algorithm":[21,69],"that,":[22],"given":[23],"sets":[24],"examples,":[29],"identifies":[30],"regions":[31,43],"examples":[34],"that":[35,55],"cannot":[36],"predicted":[38],"with":[39,59,92],"low":[40,65],"error.":[41],"These":[42],"are":[44,57],"classified":[45],"as":[46],"?":[47],"equivalently":[49],"omitted":[50],"from":[51],"classification.":[52],"Assuming":[53],"only":[54],"labels":[56],"consistent":[58],"a":[60],"family":[61],"classifiers":[63],"VC":[66],"dimension,":[67],"is":[70],"shown":[71],"to":[72],"make":[73],"few":[74,78],"misclassification":[75],"errors":[76,79],"omission":[81],"in":[82],"both":[83],"covariate-shift":[86],"settings.":[87],"Previous":[88],"models":[89],"learning":[91],"different":[93],"required":[98],"assumptions":[99],"connecting":[100],"two.":[102]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}